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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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models optimised with evolutionary algorithms to address combinatorial optimisation in model design and the noisy nature of climate data. The Doctoral Researcher will receive on-the-job training in machine
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Machine Learning. Work plan: Review of the state of the art on Evolutionary Algorithms and image tampering detection; Implementation of an evolutionary algorithm for image tampering detection
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. Develop new machine learning methodologies (from artificial neural networks, decision trees, evolutionary algorithms and others) compatible with epidemiology. Produce a digital twin for national suicide and
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) section. The BEE section investigates ecological and evolutionary patterns and processes underpinning biodiversity, scaling from genes to communities and ecosystems, and how these are affected by
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Your Job: The conventional, manual co-design of algorithms and hardware is slow and inefficient. Our group develops methods and tools to automate the co-design process. The core of this project is
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biology and bioinformatics, as well as in Machine Learning (including Large Language Models). Good understanding of evolutionary and molecular biology concepts, and good statistical (data analysis) and
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January 2026 (6pm CET). The deadline for referees to submit reference letters is 14 January 2026 (6pm CET). Please check our website https://www.molgen.mpg.de/IMPRS/application for more details. Tuition